/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RuleSetModel.java
 *    Copyright (C) 2009-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.pmml.consumer;

import java.io.Serializable;
import java.util.ArrayList;

import org.w3c.dom.Element;
import org.w3c.dom.Node;
import org.w3c.dom.NodeList;

import weka.classifiers.pmml.consumer.TreeModel.MiningFunction;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.pmml.MiningSchema;

/**
 * Class implementing import of PMML RuleSetModel. Can be used as a Weka
 * classifier for prediction only (buildClassifier() raises an Exception).
 * 
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision$
 */
public class RuleSetModel extends PMMLClassifier {

    /** For serialization */
    private static final long serialVersionUID = 1993161168811020547L;

    /**
     * Abstract inner base class for Rules
     */
    static abstract class Rule implements Serializable {

        /** For serialization */
        private static final long serialVersionUID = 6236231263477446102L;

        /** The predicate for this rule */
        protected TreeModel.Predicate m_predicate;

        public Rule(Element ruleE, MiningSchema miningSchema) throws Exception {
            // Set up the predicate
            m_predicate = TreeModel.Predicate.getPredicate(ruleE, miningSchema);
        }

        /**
         * Collect the rule(s) that fire for the supplied incoming instance
         * 
         * @param input          a vector of independent and derived independent
         *                       variables
         * @param ruleCollection the array list to add any firing rules into
         */
        public abstract void fires(double[] input, ArrayList<SimpleRule> ruleCollection);

        /**
         * Get a textual description of this Rule
         * 
         * @param prefix prefix string (typically some number of spaces) to prepend
         * @param indent the number of additional spaces to add to the prefix
         * @return a description of this Rule as a String
         */
        public abstract String toString(String prefix, int indent);

    }

    /**
     * Inner class for representing simple rules
     */
    static class SimpleRule extends Rule {

        /** For serialization */
        private static final long serialVersionUID = -2612893679476049682L;

        /** The ID for the rule (optional) */
        protected String m_ID;

        /** The predicted value when the rule fires (required) */
        protected String m_scoreString;

        /**
         * The predicted value as a number (regression) or index (classification) when
         * the rule fires (required)
         */
        protected double m_score = Utils.missingValue();

        /** The number of training/test instances on which the rule fired (optional) */
        protected double m_recordCount = Utils.missingValue();

        /**
         * The number of training/test instances on which the rule fired and the
         * prediction was correct (optional)
         */
        protected double m_nbCorrect = Utils.missingValue();

        /** The confidence of the rule (optional) */
        protected double m_confidence = Utils.missingValue();

        /** The score distributions for this rule (if any) */
        protected ArrayList<TreeModel.ScoreDistribution> m_scoreDistributions = new ArrayList<TreeModel.ScoreDistribution>();

        /**
         * The relative importance of the rule. May or may not be equal to the
         * confidence (optional).
         */
        protected double m_weight = Utils.missingValue();

        public String toString(String prefix, int indent) {
            StringBuffer temp = new StringBuffer();

            for (int i = 0; i < indent; i++) {
                prefix += " ";
            }

            temp.append(prefix + "Simple rule: " + m_predicate + "\n");
            temp.append(prefix + " => " + m_scoreString + "\n");
            if (!Utils.isMissingValue(m_recordCount)) {
                temp.append(prefix + " recordCount: " + m_recordCount + "\n");
            }
            if (!Utils.isMissingValue(m_nbCorrect)) {
                temp.append(prefix + "   nbCorrect: " + m_nbCorrect + "\n");
            }
            if (!Utils.isMissingValue(m_confidence)) {
                temp.append(prefix + "  confidence: " + m_confidence + "\n");
            }
            if (!Utils.isMissingValue(m_weight)) {
                temp.append(prefix + "      weight: " + m_weight + "\n");
            }

            return temp.toString();
        }

        public String toString() {
            return toString("", 0);
        }

        /**
         * Constructor for a simple rule
         * 
         * @param ruleE        the XML element holding the simple rule
         * @param miningSchema the mining schema to use
         * @throws Exception if something goes wrong
         */
        public SimpleRule(Element ruleE, MiningSchema miningSchema) throws Exception {
            super(ruleE, miningSchema);

            String id = ruleE.getAttribute("id");
            if (id != null && id.length() > 0) {
                m_ID = id;
            }

            m_scoreString = ruleE.getAttribute("score");
            Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute();
            if (classAtt.isNumeric()) {
                m_score = Double.parseDouble(m_scoreString);
            } else {
                if (classAtt.indexOfValue(m_scoreString) < 0) {
                    throw new Exception("[SimpleRule] class value " + m_scoreString + "does not exist in class attribute " + classAtt.name());
                }
                m_score = classAtt.indexOfValue(m_scoreString);
            }

            String recordCount = ruleE.getAttribute("recordCount");
            if (recordCount != null && recordCount.length() > 0) {
                m_recordCount = Double.parseDouble(recordCount);
            }

            String nbCorrect = ruleE.getAttribute("nbCorrect");
            if (nbCorrect != null && nbCorrect.length() > 0) {
                m_nbCorrect = Double.parseDouble(nbCorrect);
            }

            String confidence = ruleE.getAttribute("confidence");
            if (confidence != null && confidence.length() > 0) {
                m_confidence = Double.parseDouble(confidence);
            }

            String weight = ruleE.getAttribute("weight");
            if (weight != null && weight.length() > 0) {
                m_weight = Double.parseDouble(weight);
            }

            // get the ScoreDistributions (if any)
            if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
                // see if we have any ScoreDistribution entries
                NodeList scoreChildren = ruleE.getChildNodes();

                for (int i = 0; i < scoreChildren.getLength(); i++) {
                    Node child = scoreChildren.item(i);
                    if (child.getNodeType() == Node.ELEMENT_NODE) {
                        String tagName = ((Element) child).getTagName();
                        if (tagName.equals("ScoreDistribution")) {
                            TreeModel.ScoreDistribution newDist = new TreeModel.ScoreDistribution((Element) child, miningSchema, m_recordCount);
                            m_scoreDistributions.add(newDist);
                        }
                    }
                }

                // check that we have as many score distribution elements as there
                // are class labels in the data
                if (m_scoreDistributions.size() > 0 && m_scoreDistributions.size() != miningSchema.getFieldsAsInstances().classAttribute().numValues()) {
                    throw new Exception("[SimpleRule] Number of score distribution elements is " + " different than the number of class labels!");
                }

                // backfit the confidence values (if necessary)
                if (Utils.isMissingValue(m_recordCount)) {
                    double baseCount = 0;
                    for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                        baseCount += s.getRecordCount();
                    }

                    for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                        s.deriveConfidenceValue(baseCount);
                    }
                }
            }
        }

        /**
         * Collect the rule(s) that fire for the supplied incoming instance
         * 
         * @param input          a vector of independent and derived independent
         *                       variables
         * @param ruleCollection the array list to add any firing rules into
         */
        public void fires(double[] input, ArrayList<SimpleRule> ruleCollection) {
            if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
                ruleCollection.add(this);
            }
        }

        /**
         * Score the incoming instance
         * 
         * @param instance a vector containing the incoming independent and derived
         *                 independent variables
         * @param classAtt the class attribute
         * @param rsm      the rule selection method (ignored by simple rules)
         * @return a probability distribution over the class labels or the predicted
         *         value (in element zero of the array if the class is numeric)
         * @throws Exception if something goes wrong
         */
        public double[] score(double[] instance, Attribute classAtt) throws Exception {

            double[] preds;
            if (classAtt.isNumeric()) {
                preds = new double[1];
                preds[0] = m_score;
            } else {
                preds = new double[classAtt.numValues()];
                if (m_scoreDistributions.size() > 0) {
                    for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                        preds[s.getClassLabelIndex()] = s.getConfidence();
                    }
                } else if (!Utils.isMissingValue(m_confidence)) {
                    preds[classAtt.indexOfValue(m_scoreString)] = m_confidence;
                } else {
                    preds[classAtt.indexOfValue(m_scoreString)] = 1.0;
                }
            }

            return preds;
        }

        /**
         * Get the weight of the rule
         * 
         * @return the weight of the rule
         */
        public double getWeight() {
            return m_weight;
        }

        /**
         * Get the ID of the rule
         * 
         * @return the ID of the rule
         */
        public String getID() {
            return m_ID;
        }

        /**
         * Get the predicted value of this rule (either a number for regression problems
         * or an index of a class label for classification problems)
         * 
         * @return the predicted value of this rule
         */
        public double getScore() {
            return m_score;
        }
    }

    /**
     * Inner class representing a compound rule
     */
    static class CompoundRule extends Rule {

        /** For serialization */
        private static final long serialVersionUID = -2853658811459970718L;

        /** The child rules of this compound rule */
        ArrayList<Rule> m_childRules = new ArrayList<Rule>();

        public String toString(String prefix, int indent) {
            StringBuffer temp = new StringBuffer();

            for (int i = 0; i < indent; i++) {
                prefix += " ";
            }

            temp.append(prefix + "Compound rule: " + m_predicate + "\n");

            for (Rule r : m_childRules) {
                temp.append(r.toString(prefix, indent + 1));
            }

            return temp.toString();
        }

        public String toString() {
            return toString("", 0);
        }

        /**
         * Constructor.
         * 
         * @param ruleE        XML node holding the rule
         * @param miningSchema the mining schema to use
         * @throws Exception if something goes wrong
         */
        public CompoundRule(Element ruleE, MiningSchema miningSchema) throws Exception {

            // get the Predicate
            super(ruleE, miningSchema);

            // get the nested rules
            NodeList ruleChildren = ruleE.getChildNodes();
            for (int i = 0; i < ruleChildren.getLength(); i++) {
                Node child = ruleChildren.item(i);
                if (child.getNodeType() == Node.ELEMENT_NODE) {
                    String tagName = ((Element) child).getTagName();
                    if (tagName.equals("SimpleRule")) {
                        Rule childRule = new SimpleRule(((Element) child), miningSchema);
                        m_childRules.add(childRule);
                    } else if (tagName.equals("CompoundRule")) {
                        Rule childRule = new CompoundRule(((Element) child), miningSchema);
                        m_childRules.add(childRule);
                    }
                }
            }
        }

        /**
         * Collect the rule(s) that fire for the supplied incoming instance
         * 
         * @param input          a vector of independent and derived independent
         *                       variables
         * @param ruleCollection the array list to add any firing rules into
         */
        public void fires(double[] input, ArrayList<SimpleRule> ruleCollection) {

            // evaluate our predicate first
            if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
                // now check the child rules
                for (Rule r : m_childRules) {
                    r.fires(input, ruleCollection);
                }
            }
        }
    }

    /**
     * Inner class representing a set of rules
     */
    static class RuleSet implements Serializable {

        /** For serialization */
        private static final long serialVersionUID = -8718126887943074376L;

        enum RuleSelectionMethod {
            WEIGHTEDSUM("weightedSum"), WEIGHTEDMAX("weightedMax"), FIRSTHIT("firstHit");

            private final String m_stringVal;

            RuleSelectionMethod(String name) {
                m_stringVal = name;
            }

            public String toString() {
                return m_stringVal;
            }
        }

        /**
         * The number of training/test cases to which the ruleset was applied to
         * generate support and confidence measures for individual rules (optional)
         */
        private double m_recordCount = Utils.missingValue();

        /**
         * The number of training/test cases for which the default score is correct
         * (optional)
         */
        private double m_nbCorrect = Utils.missingValue();

        /**
         * The default value to predict when no rule in the ruleset fires (as a String;
         * optional)
         */
        private String m_defaultScore;

        /**
         * The default value to predict (either a real value or an index)
         */
        private double m_defaultPrediction = Utils.missingValue();

        /**
         * The default distribution to predict when no rule in the ruleset fires
         * (nominal class only, optional)
         */
        private ArrayList<TreeModel.ScoreDistribution> m_scoreDistributions = new ArrayList<TreeModel.ScoreDistribution>();

        /**
         * The default confidence value to return along with a score when no rules in
         * the set fire (optional)
         */
        private double m_defaultConfidence = Utils.missingValue();

        /** The active rule selection method */
        private RuleSelectionMethod m_currentMethod;

        /** The selection of rule selection methods allowed */
        private ArrayList<RuleSelectionMethod> m_availableRuleSelectionMethods = new ArrayList<RuleSelectionMethod>();

        /** The rules contained in the rule set */
        private ArrayList<Rule> m_rules = new ArrayList<Rule>();

        /*
         * (non-Javadoc)
         * 
         * @see java.lang.Object#toString()
         */
        public String toString() {
            StringBuffer temp = new StringBuffer();

            temp.append("Rule selection method: " + m_currentMethod + "\n");
            if (m_defaultScore != null) {
                temp.append("Default prediction: " + m_defaultScore + "\n");

                if (!Utils.isMissingValue(m_recordCount)) {
                    temp.append("       recordCount: " + m_recordCount + "\n");
                }
                if (!Utils.isMissingValue(m_nbCorrect)) {
                    temp.append("         nbCorrect: " + m_nbCorrect + "\n");
                }
                if (!Utils.isMissingValue(m_defaultConfidence)) {
                    temp.append(" defaultConfidence: " + m_defaultConfidence + "\n");
                }

                temp.append("\n");
            }

            for (Rule r : m_rules) {
                temp.append(r + "\n");
            }

            return temp.toString();
        }

        /**
         * Constructor for a RuleSet.
         * 
         * @param ruleSetNode  the XML node holding the RuleSet
         * @param miningSchema the mining schema to use
         * @throws Exception if something goes wrong
         */
        public RuleSet(Element ruleSetNode, MiningSchema miningSchema) throws Exception {

            String recordCount = ruleSetNode.getAttribute("recordCount");
            if (recordCount != null && recordCount.length() > 0) {
                m_recordCount = Double.parseDouble(recordCount);
            }

            String nbCorrect = ruleSetNode.getAttribute("nbCorrect");
            if (nbCorrect != null & nbCorrect.length() > 0) {
                m_nbCorrect = Double.parseDouble(nbCorrect);
            }

            String defaultScore = ruleSetNode.getAttribute("defaultScore");
            if (defaultScore != null && defaultScore.length() > 0) {
                m_defaultScore = defaultScore;

                Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute();
                if (classAtt == null) {
                    throw new Exception("[RuleSet] class attribute not set!");
                }

                if (classAtt.isNumeric()) {
                    m_defaultPrediction = Double.parseDouble(defaultScore);
                } else {
                    if (classAtt.indexOfValue(defaultScore) < 0) {
                        throw new Exception("[RuleSet] class value " + defaultScore + " not found!");
                    }
                    m_defaultPrediction = classAtt.indexOfValue(defaultScore);
                }
            }

            String defaultConfidence = ruleSetNode.getAttribute("defaultConfidence");
            if (defaultConfidence != null && defaultConfidence.length() > 0) {
                m_defaultConfidence = Double.parseDouble(defaultConfidence);
            }

            // get the rule selection methods
            NodeList selectionNL = ruleSetNode.getElementsByTagName("RuleSelectionMethod");
            for (int i = 0; i < selectionNL.getLength(); i++) {
                Node selectN = selectionNL.item(i);
                if (selectN.getNodeType() == Node.ELEMENT_NODE) {
                    Element sN = (Element) selectN;
                    String criterion = sN.getAttribute("criterion");
                    for (RuleSelectionMethod m : RuleSelectionMethod.values()) {
                        if (m.toString().equals(criterion)) {
                            m_availableRuleSelectionMethods.add(m);
                            if (i == 0) {
                                // set the default (first specified one)
                                m_currentMethod = m;
                            }
                        }
                    }
                }
            }

            if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
                // see if we have any ScoreDistribution entries
                NodeList scoreChildren = ruleSetNode.getChildNodes();
                for (int i = 0; i < scoreChildren.getLength(); i++) {
                    Node child = scoreChildren.item(i);
                    if (child.getNodeType() == Node.ELEMENT_NODE) {
                        String tagName = ((Element) child).getTagName();
                        if (tagName.equals("ScoreDistribution")) {
                            TreeModel.ScoreDistribution newDist = new TreeModel.ScoreDistribution((Element) child, miningSchema, m_recordCount);
                            m_scoreDistributions.add(newDist);
                        }
                    }
                }

                // backfit the confidence values (if necessary)
                if (Utils.isMissingValue(m_recordCount)) {
                    double baseCount = 0;
                    for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                        baseCount += s.getRecordCount();
                    }

                    for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                        s.deriveConfidenceValue(baseCount);
                    }
                }
            }

            // Get the rules in this rule set
            NodeList ruleChildren = ruleSetNode.getChildNodes();
            for (int i = 0; i < ruleChildren.getLength(); i++) {
                Node child = ruleChildren.item(i);
                if (child.getNodeType() == Node.ELEMENT_NODE) {
                    String tagName = ((Element) child).getTagName();
                    if (tagName.equals("SimpleRule")) {
                        Rule tempRule = new SimpleRule(((Element) child), miningSchema);
                        m_rules.add(tempRule);
                    } else if (tagName.equals("CompoundRule")) {
                        Rule tempRule = new CompoundRule(((Element) child), miningSchema);
                        m_rules.add(tempRule);
                    }
                }
            }
        }

        /**
         * Score an incoming instance by collecting all rules that fire.
         * 
         * @param instance a vector of incoming attribte and derived field values
         * @param classAtt the class attribute
         * @return a predicted probability distribution
         * @throws Exception is something goes wrong
         */
        protected double[] score(double[] instance, Attribute classAtt) throws Exception {

            double[] preds = null;
            if (classAtt.isNumeric()) {
                preds = new double[1];
            } else {
                preds = new double[classAtt.numValues()];
            }

            // holds the rules that fire for this test case
            ArrayList<SimpleRule> firingRules = new ArrayList<SimpleRule>();

            for (Rule r : m_rules) {
                r.fires(instance, firingRules);
            }

            if (firingRules.size() > 0) {
                if (m_currentMethod == RuleSelectionMethod.FIRSTHIT) {
                    preds = firingRules.get(0).score(instance, classAtt);
                } else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDMAX) {
                    double wMax = Double.NEGATIVE_INFINITY;
                    SimpleRule best = null;
                    for (SimpleRule s : firingRules) {
                        if (Utils.isMissingValue(s.getWeight())) {
                            throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDMAX, but " + "rule " + s.getID() + " does not have a weight defined!");
                        }
                        if (s.getWeight() > wMax) {
                            wMax = s.getWeight();
                            best = s;
                        }
                    }
                    if (best == null) {
                        throw new Exception("[RuleSet] Unable to determine the best rule under " + "the WEIGHTEDMAX criterion!");
                    }
                    preds = best.score(instance, classAtt);
                } else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDSUM) {
                    double sumOfWeights = 0;
                    for (SimpleRule s : firingRules) {
                        if (Utils.isMissingValue(s.getWeight())) {
                            throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDSUM, but " + "rule " + s.getID() + " does not have a weight defined!");
                        }
                        if (classAtt.isNumeric()) {
                            sumOfWeights += s.getWeight();
                            preds[0] += (s.getScore() * s.getWeight());
                        } else {
                            preds[(int) s.getScore()] += s.getWeight();
                        }
                    }
                    if (classAtt.isNumeric()) {
                        if (sumOfWeights == 0) {
                            throw new Exception("[RuleSet] Sum of weights is zero!");
                        }
                        preds[0] /= sumOfWeights;
                    } else {
                        // array gets normalized in the distributionForInstance() method
                    }
                }
            } else {
                // default prediction
                if (classAtt.isNumeric()) {
                    preds[0] = m_defaultPrediction;
                } else {
                    if (m_scoreDistributions.size() > 0) {
                        for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                            preds[s.getClassLabelIndex()] = s.getConfidence();
                        }
                    } else if (!Utils.isMissingValue(m_defaultConfidence)) {
                        preds[(int) m_defaultPrediction] = m_defaultConfidence;
                    } else {
                        preds[(int) m_defaultPrediction] = 1.0;
                    }
                }
            }

            return preds;
        }
    }

    /** The mining function */
    protected MiningFunction m_functionType = MiningFunction.CLASSIFICATION;

    /** The model name (if defined) */
    protected String m_modelName;

    /** The algorithm name (if defined) */
    protected String m_algorithmName;

    /** The set of rules */
    protected RuleSet m_ruleSet;

    /**
     * Constructor for a RuleSetModel
     * 
     * @param model          the XML element encapsulating the RuleSetModel
     * @param dataDictionary the data dictionary to use
     * @param miningSchema   the mining schema to use
     * @throws Exception if something goes wrong
     */
    public RuleSetModel(Element model, Instances dataDictionary, MiningSchema miningSchema) throws Exception {

        super(dataDictionary, miningSchema);

        if (!getPMMLVersion().equals("3.2")) {
            // TODO: might have to throw an exception and only support 3.2
        }

        String fn = model.getAttribute("functionName");
        if (fn.equals("regression")) {
            m_functionType = MiningFunction.REGRESSION;
        }

        String modelName = model.getAttribute("modelName");
        if (modelName != null && modelName.length() > 0) {
            m_modelName = modelName;
        }

        String algoName = model.getAttribute("algorithmName");
        if (algoName != null && algoName.length() > 0) {
            m_algorithmName = algoName;
        }

        NodeList ruleset = model.getElementsByTagName("RuleSet");
        if (ruleset.getLength() == 1) {
            Node ruleSetNode = ruleset.item(0);
            if (ruleSetNode.getNodeType() == Node.ELEMENT_NODE) {
                m_ruleSet = new RuleSet((Element) ruleSetNode, miningSchema);
            }
        } else {
            throw new Exception("[RuleSetModel] Should only have a single RuleSet!");
        }
    }

    /**
     * Classifies the given test instance. The instance has to belong to a dataset
     * when it's being classified.
     * 
     * @param inst the instance to be classified
     * @return the predicted most likely class for the instance or
     *         Utils.missingValue() if no prediction is made
     * @exception Exception if an error occurred during the prediction
     */
    public double[] distributionForInstance(Instance inst) throws Exception {
        if (!m_initialized) {
            mapToMiningSchema(inst.dataset());
        }
        double[] preds = null;

        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
            preds = new double[1];
        } else {
            preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute().numValues()];
        }

        double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);

        preds = m_ruleSet.score(incoming, m_miningSchema.getFieldsAsInstances().classAttribute());

        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
            Utils.normalize(preds);
        }

        return preds;
    }

    /**
     * Return a textual description of this model.
     * 
     * @return a textual description of this model
     */
    public String toString() {
        StringBuffer temp = new StringBuffer();

        temp.append("PMML version " + getPMMLVersion());
        if (!getCreatorApplication().equals("?")) {
            temp.append("\nApplication: " + getCreatorApplication());
        }
        temp.append("\nPMML Model: RuleSetModel");
        temp.append("\n\n");
        temp.append(m_miningSchema);

        if (m_algorithmName != null) {
            temp.append("\nAlgorithm: " + m_algorithmName + "\n");
        }

        temp.append(m_ruleSet);

        return temp.toString();
    }

}
